In recent years, there has been increasing an interest on a technology in which a single or multiple cameras are mounted on a vehicle to obtain information related to a safety driving such as vehicles, pedestrians, and traffic lanes around the vehicle through an image recognition.
The current technology level merely highlights to display the safety driving information extracted from an image onto the image itself used to pull out the information on a basis of augmented reality concept, however, in the future. However, in order to exhibit real-time dynamic information onto a driver's eye location through a HUD (Head-Up-Display) device, space coordinates of the safety driving information, which is obtained by the camera coordinate system, should be precisely converted to a vehicle coordinate system. In other words, it is possible to express information through the matching when the driver's eye position and the information around the vehicle acquired from the camera are accurately referenced through a common coordinate system.
Moreover, if a number of cameras are mounted in a vehicle, the integration of coordinate systems is more important in order to integrate a lot of video information. For example, when real-time space information, which is acquired from a full 360 degree around the vehicle, is converted into a vehicle coordinate system in unity, it is possible to generate a map for safe driving information around the vehicle moving in real time.
When a camera is mounted on a vehicle, correcting coordinate axes is realistically impossible in order to perfectly match a vehicle coordinate system with a camera coordinate system. The distance between the origins, that is, the distance from an origin of the vehicle coordinate system to an origin of a camera can be calculated precisely using a measuring instrument at the mount of the camera on the vehicle, but an angular misalignment is difficult to measure directly. Further, because a mounted angle always varies due to the impact on the vehicle when driving, a twist, etc., the angle needs the calibration cyclically or whenever an event occurs.
Typically, an angular misalignment calibration is also called as an angular boresight calibration, angular misalignment calibration, or the like, which is a technique widely used in a mapping equipments for acquiring images such as an aerial photogrammetry, LiDAR (Light Detection and Ranging), street views. Because these applications require a high accuracy calibration, the calibration destination precisely surveyed/measured in 3-dimensional is prepared in advance and data for the site of the calibration destination is acquired to strictly and accurately obtain a coordinate axis angular misalignment between an inertial navigation system and a camera or an inertial navigation system and LiDAR, before utilization thereof.
The pre-precision calibration process cannot be done in general vehicles every time, and the angular misalignment cannot be kept stable in the long term due to a rolling of the vehicle as described above, so there is a need to perform the calibration automatically at a time interval or when an event such as a shock occurs.